Elucidating Microglial Heterogeneity and Functions in Alzheimer’s Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction

• Published in: Scientific reports Vol. 14; no. 1; pp. 17271 - 11
• Main Authors: Wu, Xinyi, Liu, Mingyu, Zhang, Xinyue, Pan, Xue, Cui, Xiaotong, Jin, Jiahui, Sun, Huanan, Xiao, Chuyu, Tong, Xiangyi, Ren, Liou, Wang, Yaxuan, Cao, Xuezhao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/48; 631/378; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer's disease; Cell adhesion; Cell interactions; Central nervous system; Convolutional neural network; Deep Learning; Gene Expression Profiling; Gene Regulatory Networks; Heterogeneity; Humanities and Social Sciences; Humans; Microglia; Microglia - metabolism; Microglia - pathology; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Science; Science (multidisciplinary); scRNA-seq; Single-Cell Analysis - methods; Synapses; Therapeutic applications; Therapeutic targets; scRNA-seq; Alzheimer’s disease; Convolutional neural network; Microglia
• DOI: 10.1038/s41598-024-67537-1
• ISSN: 2045-2322

In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional neural network (CNN) model. Focusing on the pivotal role of microglia in AD pathology, our analysis revealed 11 distinct microglial subclusters, with 4 exhibiting obviously alterations in AD and HC groups. The investigation of cell–cell communication networks unveiled intricate interactions between AD-related microglia and various cell types within the central nervous system (CNS). Integration of WGCNA and scRNA-seq facilitated the identification of critical genes associated with AD-related microglia, providing insights into their involvement in processes such as peptide chain elongation, synapse-related functions, and cell adhesion. The identification of 9 hub genes, including USP3, through the least absolute shrinkage and selection operator (LASSO) and COX regression analyses, presents potential therapeutic targets. Furthermore, the development of a CNN-based model showcases the application of deep learning in enhancing diagnostic accuracy for AD. Overall, our findings significantly contribute to unraveling the molecular intricacies of microglial responses in AD, offering promising avenues for targeted therapeutic interventions and improved diagnostic precision.